{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The ``inline`` flag will use the appropriate backend to make figures appear inline in the notebook.  \n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# `plt` is an alias for the `matplotlib.pyplot` module\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('classic')\n",
    "\n",
    "# import seaborn library (wrapper of matplotlib)\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load car loan data into a pandas dataframe from a csv file\n",
    "filename = 'data/table_i702t60.csv'\n",
    "df = pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>starting_balance</th>\n",
       "      <th>interest_paid</th>\n",
       "      <th>principal_paid</th>\n",
       "      <th>new_balance</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>car_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>34689.96</td>\n",
       "      <td>202.93</td>\n",
       "      <td>484.30</td>\n",
       "      <td>34205.66</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34205.66</td>\n",
       "      <td>200.10</td>\n",
       "      <td>487.13</td>\n",
       "      <td>33718.53</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33718.53</td>\n",
       "      <td>197.25</td>\n",
       "      <td>489.98</td>\n",
       "      <td>33228.55</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>33228.55</td>\n",
       "      <td>194.38</td>\n",
       "      <td>492.85</td>\n",
       "      <td>32735.70</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32735.70</td>\n",
       "      <td>191.50</td>\n",
       "      <td>495.73</td>\n",
       "      <td>32239.97</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>Toyota Sienna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  starting_balance  interest_paid  principal_paid  new_balance  \\\n",
       "0      1          34689.96         202.93          484.30     34205.66   \n",
       "1      2          34205.66         200.10          487.13     33718.53   \n",
       "2      3          33718.53         197.25          489.98     33228.55   \n",
       "3      4          33228.55         194.38          492.85     32735.70   \n",
       "4      5          32735.70         191.50          495.73     32239.97   \n",
       "\n",
       "   interest_rate       car_type  \n",
       "0         0.0702  Toyota Sienna  \n",
       "1         0.0702  Toyota Sienna  \n",
       "2         0.0702  Toyota Sienna  \n",
       "3         0.0702  Toyota Sienna  \n",
       "4         0.0702  Toyota Sienna  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the first 5 rows of the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60 entries, 0 to 59\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   month             60 non-null     int64  \n",
      " 1   starting_balance  60 non-null     float64\n",
      " 2   interest_paid     60 non-null     float64\n",
      " 3   principal_paid    60 non-null     float64\n",
      " 4   new_balance       60 non-null     float64\n",
      " 5   interest_rate     60 non-null     float64\n",
      " 6   car_type          60 non-null     object \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 3.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking to make sure we dont have nans in our dataframe\n",
    "# It is not easy to directly plot data that contains nans\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this notebook we will graph interest_paid, principal_paid, and month on one graph\n",
    "# While we could graph directly through pandas, we will graph through matplotlib for now.\n",
    "month_number = df.loc[:, 'month'].values\n",
    "interest_paid = df.loc[:, 'interest_paid'].values\n",
    "principal_paid = df.loc[:, 'principal_paid'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "       52, 53, 54, 55, 56, 57, 58, 59, 60])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The values attribute converts a column of values into a numpy array\n",
    "type(month_number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Legends\n",
    "\n",
    "The `loc` (legend location) parameter accepts strings, ints, and tuples\n",
    "\n",
    "string | int\n",
    "--- | ---\n",
    "'best' | 0\n",
    "'upper right' | 1\n",
    "'upper left' | 2\n",
    "'lower left' |3\n",
    "'lower right' | 4\n",
    "'right' | 5\n",
    "'center left' | 6\n",
    "'center right' | 7\n",
    "'lower center' | 8\n",
    "'upper center' | 9\n",
    "'center' | 10\n",
    "\n",
    "The parameter accepts a 2 element tuple  ``x, y`` where (0, 0) is the of the lower-leftcorner of the legend in axes coordinates. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MATLAB-style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x101eb09d90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obviously the legend is not in an ideal location\n",
    "plt.plot(month_number, interest_paid, c= 'k', label = 'Interest')\n",
    "plt.plot(month_number, principal_paid, c = 'b', label = 'Principal')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a21924f90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# At least the legend is not overlapping with the graph\n",
    "plt.plot(month_number, interest_paid, c= 'k', label = 'Interest')\n",
    "plt.plot(month_number, principal_paid, c = 'b', label = 'Principal')\n",
    "plt.legend(loc=\"center right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can move the legend outside of the plotting area. \n",
    "# At least the legend is not overlapping with the graph\n",
    "plt.plot(month_number, interest_paid, c= 'k', label = 'Interest')\n",
    "plt.plot(month_number, principal_paid, c = 'b', label = 'Principal')\n",
    "plt.legend(loc=(1.02,0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Object-oriented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obviously the legend is not in an ideal location\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 1);\n",
    "axes.plot(month_number, interest_paid, c= 'k', label = 'Interest');\n",
    "axes.plot(month_number, principal_paid, c = 'b', label = 'Principal');\n",
    "axes.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# At least the legend is not overlapping with the graph\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 1);\n",
    "axes.plot(month_number, interest_paid, c= 'k', label = 'Interest');\n",
    "axes.plot(month_number, principal_paid, c = 'b', label = 'Principal');\n",
    "axes.legend(loc=\"center right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# At least the legend is not overlapping with the graph\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 1);\n",
    "axes.plot(month_number, interest_paid, c= 'k', label = 'Interest');\n",
    "axes.plot(month_number, principal_paid, c = 'b', label = 'Principal');\n",
    "axes.legend(loc=(1.02,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
